Facial pose estimation using modified synergetic computer

In this paper, a facial pose estimation based on synergetic computer approach is introduced. Previous researches have proven that basic synergetic computer is not sufficient to support advanced tasks, especially in the field of facial pose estimation problem, which require more than one pattern to be combined into a single class. In this work, we first investigate one of the extensions of basic synergetic computer, SC-MELT. Original SC-MELT exhibits overfitting phenomenon due to its inverse matrix method used which may deteriorate the classification performance. Moreover, its inflexibility for tuning the “melting level” necessitates modification. For this reason, we applied the notion of MPOD penalty function to modify the original SC-MELT and it yielded Modified SC-MELT. We have applied these techniques to model each facial pose cluster into a view-based adjoint prototype. Thus, a query image is projected to each adjoint prototype to compute the responses for facial pose estimation. Experimental results demonstrate that Modified SC-MELT presents considerable improvement in classification rate as compared to original SC-MELT.